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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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敌对转移的数据辅助软传感器用于增强多级质量预测.

Yun Dai1, Chao Yang2, Jialiang Zhu1

  • 1Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, People's Republic of China.

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一种新的即时对抗转移学习 (JATL) 软传感方法改善了多级化学过程的预测. 这种方法通过调整分布和选择相关数据来提高新年级的表现,使用有限的数据.

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科学领域:

  • 化学工程是化学工程的重要组成部分.
  • 过程控制 过程控制
  • 机器学习 机器学习

背景情况:

  • 转移学习软传感器在多级化学过程中表现有前途.
  • 高预测性能需要足够的目标域数据,通常无法用于新等级.
  • 单个全球模型难以捕捉多级过程变量中的复杂关系.

研究的目的:

  • 开发一种新的软传感方法,用于增强多级化学过程预测.
  • 为应对转移学习中的新操作等级数据稀缺的挑战.
  • 改进不同级别的过程变量关系的表征.

主要方法:

  • 实施了对抗性转移学习 (ATL) 策略,以尽量减少等级之间的分配差异.
  • 采用了即时学习 (JIT) 方法,从转移的源数据中选择相关数据.
  • 为质量预测开发了一个即时对抗转移学习 (JATL) 软传感框架.

主要成果:

  • 该JATL方法成功地减少了运行等级之间的分配差异.
  • 可靠的软传感器模型是使用JIT从传输数据中选择数据来构建的.
  • 新目标等级的质量预测可以实现,而不需要标记数据.

结论:

  • 在多级化学过程中,JATL软传感方法显著提高了预测性能.
  • 这种方法有效地克服了新年级数据稀缺性的局限性.
  • 在两个化学工艺上的实验验证证证了JATL方法的优越性.